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Recommended Price Optimization in Convenience Franchising: A Data-Driven Strategy for a Retail Network
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Orientador(es)
Resumo(s)
This thesis explores a pricing recommendation strategy built for a convenience store chain for
the franchisees. The objective is to build a hybrid classification between business logic and
machine learning to construct a classification model of the store's product range based on
sales and profit. The process starts with business understanding and ends with model
evaluation, following the CRISP-DM methodology. Initially, a manual and arbitrary
classification was created, where a score based on static thresholds would classify the product
type using regular (non-promotional) sales quantity and the franchisee's profit margin.
Despite this, this approach has limitations: it is subjective, static, and may not adapt to future
market changes. Machine learning overcomesthese limitations by integrating algorithmssuch
as Random Forest, KNN and Naive Bayes for validation and to automate classifications. To
train and build this classification, 2024 sales data were collected, including margins, prices,
and sales across all stores, to study product behavior and classify them strategically into
essential, medium and premium. By classifying through algorithms and with the learned
models and accurate results, product classifications will allow the pricing strategy to become
more automated and to better respond to changes in demand. By associating these
classifications with differentiated pricing strategies, the model strengthens the effectiveness
of commercial decisions in a dynamic retail context.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Pricing Strategy Convenience Stores Franchising Recommended Price Optimization Consumer Behavior SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 10 - Reduced inequalities SDG 12 - Responsible production and consumption SDG 17 - Partnerships for the goals